Emotion Text Classifier (RoBERTa)
A fine-tuned roberta-base model for classifying text into 6 emotions:
sadness, joy, love, anger, fear, surprise.
Training Details
- Base model:
roberta-base - Dataset: dair-ai/emotion (20k train / 2k val / 2k test)
- Epochs: 5
- Learning rate: 2e-5
- Batch size: 16
- Weight decay: 0.01
- Best model selection: accuracy on validation set
- Mixed precision: fp16 (trained on T4 GPU)
Results
Update these with your actual results after training:
| Metric | Score |
|---|---|
| Test Accuracy | ~93% |
| Weighted F1 | ~93% |
Usage
from transformers import pipeline
classifier = pipeline("text-classification", model="dk409/emotion-roberta", top_k=None)
result = classifier("I'm so happy today!")
print(result)
# [[{{'label': 'joy', 'score': 0.98}}, {{'label': 'love', 'score': 0.01}}, ...]]
Labels
| ID | Label |
|---|---|
| 0 | sadness |
| 1 | joy |
| 2 | love |
| 3 | anger |
| 4 | fear |
| 5 | surprise |
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